• Own our canonical data architecture — the schema, contracts, tenancy, and governance. • Make production data AI-ready: well-modeled, contract-enforced, lineage-tracked, and drift-detectable. • Own the canonical data model — the normalized definition of the core business objects shared across our products. • Define the multi-tenant data architecture: tenancy isolation, data residency posture, and per-tenant cost attribution. • Lead staged modernization toward the right mix of stores and patterns. • Own the architectural direction of the data pipeline and lake / lakehouse layer. • Drive hands-on prototypes, reference implementations, and in-repo guardrails. • Partner with database engineering on production data health while owning long-term architectural direction.

• Define and document end-to-end data architectures, from source ingestion to analytical consumption layers, ensuring scalability, performance, and governance • Establish technical standards, development guidelines, and architectural decisions to promote consistency across projects and teams • Lead adoption and evolution of Lakehouse architecture (Medallion Architecture) in Databricks and Azure environments, including partitioning, clustering, and Delta Table optimization strategies • Define and guide data ingestion strategies for batch, incremental, CDC, and streaming scenarios • Provide technical leadership for data modeling decisions appropriate to the analytical context and consumption patterns • Ensure implementation of data governance practices (cataloging, lineage, access control at object/row/column levels) • Act in a consultative capacity with clients, conducting technical discovery, gathering requirements, and presenting architectural proposals • Support and review the work of data engineers, ensuring adherence to defined standards and promoting development best practices • Collaborate with BI, Engineering, and AI teams to define the data layers that support analytical, semantic, and ML models • Evaluate and recommend technologies, tools, and integration patterns aligned with the Azure + Databricks ecosystem • Monitor the health of data platforms (quality, SLAs, compute and storage costs) and propose continuous improvements • Contribute to pre-sales activities and technical qualification of opportunities by developing reference architectures and effort estimates.

• Participar en la definición y ejecución de estrategias de migración de datos. • Realizar análisis de estructuras origen y destino. • Implementar, administrar y validar estructuras de bases de datos. • Garantizar la integridad, consistencia y calidad de los datos migrados. • Ejecutar procesos de validación y conciliación de información. • Participar en pruebas funcionales, de carga y rendimiento. • Gestionar incidencias relacionadas con bases de datos durante la ejecución del proyecto. • Elaborar documentación técnica y evidencias de migración. • Apoyar la definición y ejecución de estrategias de gestión de datos históricos. • Analizar y definir proceso de integraciones con sistemas satélite y terceros.

Role Description The Data Engineer is responsible for building and scaling the data and computational backbone that supports Arva’s ecosystem modeling and measurement, reporting, and verification platforms. This role sits within a multidisciplinary Data Science team and focuses on designing reliable, auditable, and scalable data systems that enable biogeochemical modeling and optimization at production scale. In this role, the Data Engineer will design and maintain production-grade data pipelines that integrate diverse datasets including field measurements, management practices, soils, and weather with process-based ecosystem models. The role plays a critical part in ensuring data quality, reproducibility, and traceability so that scientific outputs can be translated into trusted, credit-grade results with real-world impact. Qualifications - 3+ years demonstrated experience building and maintaining data pipelines for large, complex, and heterogeneous datasets - Strong proficiency in Python and modern data engineering tools, with experience writing production-grade, testable code - Experience working with cloud platforms, with AWS strongly preferred - Familiarity with containerization tools such as Docker and version control systems such as GitHub - Experience with relational and spatial databases, including PostgreSQL and PostGIS - Experience working with geospatial data formats and spatial data processing - Experience supporting scientific or ecosystem modeling workflows preferred - Familiarity with workflow orchestration tools such as Airflow or Prefect preferred - Bachelor’s or Master’s degree or equivalent experience in Data Engineering, Computer Science, Environmental Informatics, or a related field Requirements - Design, implement, and maintain scalable data pipelines supporting ecosystem and biogeochemical modeling - Build reproducible workflows that generate standardized model inputs and manage outputs across space, time, and scenario analysis - Integrate heterogeneous datasets, including field data, management data, soil data, and weather data, into modeling pipelines - Develop and maintain cloud-based infrastructure to support modeling pipelines and optimization workflows - Implement data storage solutions using relational, spatial, and object-based databases - Support efficient data access and processing using platforms such as PostgreSQL, PostGIS, and cloud object storage - Ensure data quality, versioning, traceability, and auditability to support measurement, reporting, and verification requirements - Implement validation and monitoring processes to ensure reliability of model inputs and outputs - Support transparent, repeatable workflows suitable for regulatory and credit market review - Write clean, modular, and well-documented production code that supports maintainable and scalable data systems - Apply software engineering best practices including testing, version control, and documentation - Collaborate closely with Data Science and Technology teams to align data infrastructure with modeling, analytics, and production needs Benefits - $95k - $130k base salary range